Genetically programmed Learning Classifier Systems (LCS) for Complex Adaptive Systems (CAS) are known. One such system and method are disclosed in U.S. Pat. No. 6,741,974 “Genetically Programmed Learning Classifier System for Complex Adaptive System processing with agent-based architecture” which was filed Jun. 2, 2000 and is assigned to the assignee hereof. The disclosure of the '974 patent is hereby incorporated herein by reference.
The '974 patent describes a system and a method enabling a software agent to learn about its environment, and learn how to perform jobs and tasks. Information is assimilated and used in an adaptive manner by the genetically evolving rule set of the agent. The agent learning system communicates with the external environment in an artificial economy through on-line auctions. Internal information also passes through message auctions that provide chaining of rule execution. The agent continually attempts to automatically improve its performance with respect to fitness for a job.
Complex Adaptive Systems (CAS) contain many components that interact and adapt both as a result of external agency or from the occurrence of internal events. The term agent can be applied to any entity, biological, mechanical, or otherwise, that can perform actions, intelligent or not. In CAS, agents are designed to perceive their environment through its detectors and take action on its perception through its effectors. In agent-based systems, system intelligence is contained in locally encapsulated software entities, or agents, and is thus portable and dedicated to given tasks. Individual agents can adapt without changing the rest of the system. An agent is a self-contained entity in a complex adaptive system (CAS). Each agent is able to accomplish given tasks within a software environment. The use of multiple agents allows interaction and parallel execution of multiple tasks in diverse locations throughout the system. The intrinsic parallelism provides enhanced system robustness.
Many methods of controlling adversaries in virtual worlds have been designed, with varying levels of intelligence. Activities typically have been processed through static goal-driven command structures. In cases of training, and analysis of military scenarios, virtual characters that do not adapt to the changing environment provide poor adversaries, since they always tend to operate with the same plans and performance criteria, like a semi-automated forces simulation.
Modeling the battlefield as a complex adaptive system allows intelligent agents to adapt to perform their programs better. If these agents are expected to carry out missions but also to destroy each other as possible, then they will develop skills that enable them to hunt and evade as needed.
A number of patents exist which relate to controlling adversaries in virtual worlds and intelligent agents, including, U.S. Pat. Nos. 6,741,974, 6,076,099, 7,094,153, 7,319,992, 7,528,835, all of which are incorporated herein by reference. However, these patents fail to provide real-time adaptive evolutionary software to control the agents to provide new capabilities.
The present invention is designed to address these needs.